The new IT stack: Rebuilding infrastructure for an AI-first world

Not every system is built for the AI era. As new models and workloads push the limits of what legacy environments can support, IT leaders are being forced to ask: Which parts of our stack are ready for this, and which ones are holding us back?

In the Q1 2025 IT Trends Report from JumpCloud, IT decision-makers named AI-related tools (42%) and cloud infrastructure (40%) among the top spending priorities, second only to cybersecurity. That convergence signals something critical: IT leaders are not just deploying AI—they’re rethinking the infrastructure that supports it.

How AI is reshaping IT architecture

AI workloads come with unique infrastructure demands, from high-volume data pipelines to scalable compute environments. This is prompting a pivot toward more flexible, cloud-native architectures that can adapt as AI systems grow more complex and resource-intensive.

Legacy systems, particularly those with rigid data structures or limited scalability, pose real barriers. On-premises infrastructure often can’t keep pace with the computational demands of training and running AI models, while siloed or outdated security frameworks may not account for new attack surfaces introduced by machine learning.

To build for AI, IT leaders need to think in terms of adaptability and integration—designing environments that support the data gravity, model training, and dynamic access needs of AI-driven operations.

The move to the cloud is well underway, but that doesn’t mean the end of on-prem infrastructure. Hybrid approaches are increasingly common, particularly for organizations with sensitive data or latency-sensitive applications. The key is seamless integration across environments.

Workloads that involve sensitive PII or require strict compliance oversight may stay on-prem, while less regulated or compute-heavy operations move to the cloud. As one approach, organizations are starting to transition foundational services—like identity or directory platforms—away from legacy infrastructure toward cloud-based alternatives that better support AI-scale operations.

Updating security and compliance for an AI world

Security and compliance frameworks are also being tested. Traditional approaches weren’t designed for the complexities introduced by AI, including adversarial manipulation, data poisoning, and algorithmic bias.

While 48 percent of IT teams report increased investment in cybersecurity, the real challenge is evolving those frameworks to account for AI-specific risks. This includes establishing protocols for model monitoring, explainability, and access control that reflect how AI operates in dynamic environments.

Unification is becoming a core requirement. Consolidating identity, access, and device management doesn’t just reduce tool sprawl—it creates a centralized, data-rich foundation that AI can use to accelerate decision-making and automation.

When these core systems operate in silos, it’s harder to identify anomalies, enforce policy, or respond quickly to threats. A unified stack helps IT teams maintain visibility and control, even as AI systems introduce new types of interactions and access requests.

Rebuilding the IT stack for an AI-first world isn’t a teardown—it’s an evolution. It means re-evaluating which legacy systems are holding you back, embracing hybrid flexibility, and building a secure foundation that’s designed for intelligence at scale.

Interested in learning more about how your peers are thinking about AI and other critical IT trends? Download JumpCloud’s full report here.